Title

Authors

Keywords

autonomous virtual characters, motion synthesis, behavior synthesis

Abstract

In the creation of autonomous virtual characters, two levels of autonomy are common. They are often called motion synthesis (low-level autonomy) and behavior synthesis (high-level autonomy), where an action (i.e. motion) achieves a short-term goal and a behavior is a sequence of actions that achieves a long-term goal. There exists a rich literature addressing many aspects of this general problem (and it is discussed in the full paper). In this paper we present a novel technique for behavior (high-level) autonomy and utilize existing motion synthesis techniques. Creating an autonomous virtual character with behavior synthesis abilities frequently includes three stages: forming a model used to generate decisions, running the model to select a behavior to perform given the conditions in the environment, and then carrying out the chosen behavior (translating it into low-level synthesized or explicit motions). For this process to be useful it must efficiently produce realistic behaviors. We address both of these requirements with a novel technique for creating cognitive models. The technique uses programming-by-demonstration to address the first requirement, and uses data-driven behavior synthesis to address the second. Demonstrated human behavior is recorded as sequences of abstract actions, the sequences are segmented and organized into a searchable data structure, and then behavior segments are selected by determining how well they accomplish the character’s long-term goal (see Fig. 1). The resulting model allows a character to engage in a very large variety of high-level behaviors.